Spacecraft attitude control and vibration suppression using magnetically suspended control & sensitive gyroscope and radial basis function network adaptive sliding mode control

Abstract

For the sake of improving the agility and super-static performance of spacecraft under external disturbances, this paper puts forward an idea for using pyramid configuration of the Magnetically Suspended Control & Sensitive Gyroscope to realize dual control of attitude maneuver and vibration suppression of spacecraft. The RBF neural network is used to reduce the chattering in sliding mode control through adaptively learning the non-linear part of the system. A band-pass filter is designed to divide the attitude deviation signal fed back by the sensors into high-frequency and low-frequency parts. The control laws of gyro gimbal and magnetic suspension rotor are designed, respectively, to realize the attitude maneuver and vibration suppression simultaneously. The adaptive laws of parameters avoid the saturation of rotor deflection angle while speeding up the attitude maneuver process of spacecraft. Compared with the traditional sliding mode control methods, the proposed one not only improves the accuracy and speed of attitude control but also improves the robustness of the spacecraft attitude control and vibration suppression system. Semi-physical simulation results show the effectiveness and superiority of the proposed method.